Is Google Tensorflow Object Detection API the easiest way to implement image recognition?

Published Jul 13, 2017

Doing cool things with data!

There are many different ways to do image recognition. Google recently released a new Tensorflow Object Detection API to give computer vision everywhere a boost. Any offering from Google is not to be taken lightly, and so I decided to try my hands on this new API and use it on videos from you tube See the result below:

Understanding the API

The API has been trained on the COCO dataset (Common Objects in Context). This is a dataset of 300k images of 90 most commonly found objects. Examples of objects includes:

Some of the object categories in COCO datset

The API provides 5 different models that provide a trade off between speed of execution and the accuracy in placing bounding boxes. See table below:

Here mAP (mean average precision) is the product of precision and recall on detecting bounding boxes. It’s a good combined measure for how sensitive the network is to objects of interest and how well it avoids false alarms. The higher the mAP score, the more accurate the network is but that comes at the cost of execution speed.

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